First load the data and required packages

library(ggplot2); library(dplyr);library(tidyverse); library(rmcorr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ tibble  3.1.8     ✔ purrr   0.3.4
## ✔ tidyr   1.1.4     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library(bestNormalize); library(ggpubr)

df <- read.csv('Study 1 anon.csv')

Figure 1:

How do average levels of politicization and slant (perceptions) associate with trust?

names(df)[names(df) == 'Slant_K12'] <- 'slant_K12'
names(df)[names(df) == 'Polit_K12'] <- 'polit_K12'
names(df)[names(df) == 'Slant_Cong'] <- 'slant_Cong'
names(df)[names(df) == 'Polit_Cong'] <- 'polit_Cong'
names(df)[names(df) == 'Trust_Econ'] <- 'Trust_econ'
names(df)[names(df) == 'Trust_Pharm'] <- 'Trust_pharm'
names(df)[names(df) == 'Trust_Sci'] <- 'Trust_sci'

all_groups <- c()
for (n in names(df %>% select(starts_with("slant_")))){
  all_groups <- c(all_groups,substring(n,unlist(gregexpr('_', n))[1]+1))
}

trust <- c()
slant <- c()
polit <- c()
for (g in all_groups){
  trust <- c(trust,mean(df[,paste("Trust_",g,sep='')],na.rm=TRUE))
  slant <- c(slant,mean(df[,paste("slant_",g,sep='')],na.rm=TRUE))
  polit <- c(polit,mean(df[,paste("polit_",g,sep='')],na.rm=TRUE))
}
df_means <- data.frame(all_groups,trust,slant,polit)

a2 <- ggplot(df_means, aes(x=slant, y = trust)) + 
  geom_point() + geom_smooth(method=glm) + 
  ylab('Mean Org. Trust') + xlab('Mean Org. Slant') + 
  theme(axis.text=element_text(size=15), 
        axis.title=element_text(size=15), 
        axis.title.x=element_text(size=15),
        panel.background = element_rect(fill='white'),
        plot.background = element_rect(fill='transparent', color=NA),
        axis.text.x=element_text(size=8, angle=90),
        panel.border = element_rect(colour = "black", fill=NA, size=1))+
  coord_cartesian(xlim = c(20,80), ylim = c(1.5,5.0))

b2 <- ggplot(df_means, aes(x=polit, y = trust)) + 
  geom_point() + geom_smooth(method=glm) + 
  ylab('Mean Org. Trust') + xlab('Mean Org. Politicization') + 
  theme(axis.text=element_text(size=15), 
        axis.title=element_text(size=15), 
        axis.title.x=element_text(size=15),
        axis.title.y=element_blank(),
        panel.background = element_rect(fill='white'),
        plot.background = element_rect(fill='transparent', color=NA),
        axis.text.x=element_text(size=8, angle=90),
        panel.border = element_rect(colour = "black", fill=NA, size=1))+
  coord_cartesian(xlim = c(1,6), ylim = c(1.5,5.0))

ggarrange(a2, b2, labels = c("", ""),ncol = 2, nrow = 1, widths=c(1,0.93))
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

ggsave("plots/Figure1-Mean-slant-polit-trust.png",width = 8,height = 4,units = "in",dpi = 300)

cor.test(df_means$slant,df_means$trust,method='pearson',conf.level=0.95)
## 
##  Pearson's product-moment correlation
## 
## data:  df_means$slant and df_means$trust
## t = -0.90884, df = 38, p-value = 0.3692
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.4374889  0.1735369
## sample estimates:
##        cor 
## -0.1458572
cor.test(df_means$polit,df_means$trust,method='pearson',conf.level=0.95)
## 
##  Pearson's product-moment correlation
## 
## data:  df_means$polit and df_means$trust
## t = -7.1594, df = 38, p-value = 1.498e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.8650951 -0.5842007
## sample estimates:
##        cor 
## -0.7578022

Figure 2

How do folks on the left and the right differ regarding these relationships toward particular institutions?

plot_politicization <- function(df, polit_col, slant_col,include_y,include_legend) {
  df_reg <- df %>%
    select(!!sym(polit_col), !!sym(slant_col), Ideology) %>%
    drop_na() %>%
    mutate(Party = ifelse(Ideology < 4, "Left", ifelse(Ideology == 4, "Center", "Right"))) %>%
    filter(Party != "Center")
  
  df_reg$Party <- factor(df_reg$Party, levels = c('Left','Center','Right'))
  
  ggplot(df_reg, aes(x=!!sym(slant_col), y = !!sym(polit_col), color=Party,fill=Party)) + 
    geom_point(alpha = 0.5, size =0.5) + 
    geom_jitter(height = 0.5,width = 0.0,alpha = 0.5, size =0.5) +
    geom_smooth(method=glm) + 
    scale_color_manual(values=c("#0000ff",'#ff0803'))+scale_fill_manual(values=c("#0000ff",'#ff0803'))+
    ylab(ifelse(include_y, "Politicization", "")) + 
    xlab(str_to_title(str_replace(slant_col, "_", " "))) + 
    theme(axis.text=element_text(size=15), 
          axis.title=element_text(size=15), 
          axis.title.x=element_text(size=15),
          axis.title.y=element_text(size=15),
          legend.position = ifelse(include_legend,'right',"none"),
          panel.background = element_rect(fill='white'),
          plot.background = element_rect(fill='transparent', color=NA),
          axis.text.x=element_text(size=8, angle=90),
          panel.border = element_rect(colour = "black", fill=NA, size=1)) +
    coord_cartesian(xlim = c(0,100), ylim = c(0,6.5))
}

# use the function for each set of columns
a <- plot_politicization(df, "polit_vet", "slant_vet",T,F)
b <- plot_politicization(df, "polit_police", "slant_police",F,F)
c <- plot_politicization(df, "polit_prof", "slant_prof",F,T)

# arrange the plots
ggarrange(a, b, c, labels = c("", "", ""),ncol = 3, nrow = 1, widths=c(0.85,0.78,1))
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

ggsave("plots/Figure2-Ideologyxslant-politicization_1.png",width = 10,height = 3.5,units = "in",dpi = 300)

Figure 3

Next we want to look at ideological congruence for several organizations:

plot_congruence <- function(data, group,include_y,xlab_use,include_legend){
  group_prefix <- paste0("_", group)
  df_reg <- data %>%
    select(ends_with(group_prefix), Ideology) %>%
    drop_na() %>%
    mutate(Slant_2 = get(paste0('slant',group_prefix)) - 50,
           Ideo_2 = Ideology - 4,
           Matched_Bias = Slant_2 * Ideo_2,
           Congruence = case_when(
             is.na(Matched_Bias) ~ NA_character_,
             Matched_Bias < 0 ~ "Incongruent",
             Matched_Bias > 0 ~ "Congruent",
             TRUE ~ 'Neutral'
           )
    ) %>% 
    filter(Congruence != 'Neutral') %>% # Remove if you want Neutral
    drop_na()
  df_reg$Congruence <- factor(df_reg$Congruence, levels = c('Incongruent','Congruent')) #'Neutral','Congruent'))
  
  a <- ggplot(df_reg, aes(x = get(paste0("polit",group_prefix)), y = get(paste0("Trust",group_prefix)), color = Congruence, fill=Congruence)) + 
    geom_point(alpha = 0.75, size = 0.5) + 
    geom_jitter(height = 0.5, width = 0.5, alpha = 0.5, size = 0.75) + 
    geom_smooth(method = glm) + 
    scale_color_manual(values = c('#FF5733',"#007200")) + 
    scale_fill_manual(values = c('#FF5733',"#007200")) +
    #scale_color_manual(values = c('#FF5733','#d4af37',"#007200")) + # If you want neutral
    #scale_fill_manual(values = c('#FF5733','#d4af37',"#007200")) +
    ylab(ifelse(include_y, "Trust", ""))  + 
    xlab(paste0('Politicization (', xlab_use, ')')) + 
    theme(
      axis.text = element_text(size = 15), 
      axis.title = element_text(size = 15), 
      axis.title.x = element_text(size = 15),
      legend.position = ifelse(include_legend,'right',"none"),
      panel.background = element_rect(fill = 'white'),
      plot.background = element_rect(fill = 'transparent', color = NA),
      axis.text.x = element_text(size = 8, angle = 90),
      panel.border = element_rect(colour = "black", fill = NA, size = 1)
    )
  
  return(a)
}

a1 = plot_congruence(df, "vet",T,"Vets",F)
a2 = plot_congruence(df, "police",F,"Police",F)
a3 = plot_congruence(df, "prof",F,"Professors",T)
b1 = plot_congruence(df, "toll",T,"Toll Booth",F)
b2 = plot_congruence(df, "SC",F,"Supreme Court",F)
b3 = plot_congruence(df, "WHO",F,"WHO",T)
c1 = plot_congruence(df, "chefs",T,"Chefs",F)
c2 = plot_congruence(df, "church",F,"Church",F)
c3 = plot_congruence(df, "PETA",F,"PETA",T)

ggarrange(a1, a2, a3, b1, b2, b3, c1, c2, c3, labels = c("", "", "","", "", "","", "", ""),ncol = 3, nrow = 3, widths=c(0.85,0.78,1.15))
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

ggsave("plots/Figure3-Congrencextrust-politicization.png",width = 10,height = 10.5,units = "in",dpi = 300)

Figure 4

Now we only want to look at the orgs with statistically significant 3-way interactions

a1_1 = plot_congruence(df, "econ",T,"Economists",F)
b1_1 = plot_congruence(df, "holly",F,"Hollywood",F)
c1_1 = plot_congruence(df, "think",F,"Think Tanks",T)

ggarrange(a1_1, b1_1, c1_1, labels = c("", "", ""),ncol = 3, nrow = 1, widths=c(0.85,0.78,1.15))
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

ggsave("plots/Figure4-Congrencextrust-politicization.png",width = 10,height = 3.5,units = "in",dpi = 300)

Figure 5

Here, we need to load the data from study 2:

library(readxl)
df_2 <- read_excel('Study 2 anon.xlsx')

names(df_2)[names(df_2) == 'TrustWHO'] <- 'TrustWho'
names(df_2)[names(df_2) == 'TrustFIRE'] <- 'TrustFire'
names(df_2)[names(df_2) == 'SupportWHO'] <- 'SupportWho'
names(df_2)[names(df_2) == 'SupportFIRE'] <- 'SupportFire'

For this next figure, I want to look at trust for the congruent, incongruent, and neutral groups from Study2

plot_congruence_2 <- function(data, group,include_y,xlab_use,include_legend,trust){
  df_reg <- data %>%
    select(ends_with(group), Ideology) %>%
    drop_na() %>%
    mutate(
      Slant_2 = get(paste0('Slant',group)) - 4,
      Ideo_2 = Ideology - 4,
      Matched_Bias = Slant_2 * Ideo_2,
      Congruence = case_when(
        is.na(Matched_Bias) ~ NA_character_,
        Matched_Bias < 0 ~ "Incongruent",
        Matched_Bias > 0 ~ "Congruent",
        TRUE ~ "Neutral"
      )
    ) %>%
    drop_na() %>%
    mutate(Congruence = factor(Congruence, levels = c("Incongruent","Neutral", "Congruent")))
  
  a <- ggplot(df_reg, aes(x=get(paste0("Polit",group)), y = get(paste0(ifelse(trust, "Trust", "Support"),group)), color = Congruence)) + 
    geom_point(alpha = 0.5, size =0.5) + geom_jitter(height = 0.5,width = 0.5,alpha = 0.5, size =0.5) + geom_smooth(method=glm,aes(fill=Congruence)) + 
    scale_color_manual(values=c('#FF5733','#d4af37',"#007200"))+ scale_fill_manual(values=c('#FF5733','#d4af37',"#007200"))+
    ylab(ifelse(include_y, ifelse(trust, "Trust", "Support"), ""))  + 
    xlab(paste0('Politicization (', xlab_use, ')')) +
    theme(axis.text=element_text(size=15), 
          axis.title=element_text(size=15), 
          axis.title.x=element_text(size=15),
          legend.position = ifelse(include_legend,'right',"none"),
          panel.background = element_rect(fill='white'),
          plot.background = element_rect(fill='transparent', color=NA),
          axis.text.x=element_text(size=8, angle=90),
          panel.border = element_rect(colour = "black", fill=NA, size=1))
  return(a)
}

a1 = plot_congruence_2(df_2, "Prof",T,"Professors",F,T)
a2 = plot_congruence_2(df_2, "Fire",F,"Fire",F,T)
a3 = plot_congruence_2(df_2, "Police",F,"Police",T,T)
b1 = plot_congruence_2(df_2, "Who",T,"WHO",F,T)
b2 = plot_congruence_2(df_2, "Doc",F,"Doctors",F,T)
b3 = plot_congruence_2(df_2, "Judge",F,"Judges",T,T)
ggarrange(a1, a2, a3, b1, b2, b3, labels = c("", "", "","", "", "","", "", ""),ncol = 3, nrow = 2, widths=c(0.85,0.78,1.15))
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

ggsave("plots/Figure5-Study-2_Congrencextrust-politicization.png",width = 10,height = 7,units = "in",dpi = 300)

Figure 6

We will create a similar figure to Fig. 5, but with support instead of trust

a1 = plot_congruence_2(df_2, "Prof",T,"Professors",F,F)
a2 = plot_congruence_2(df_2, "Fire",F,"Fire",F,F)
a3 = plot_congruence_2(df_2, "Police",F,"Police",T,F)
b1 = plot_congruence_2(df_2, "Who",T,"WHO",F,F)
b2 = plot_congruence_2(df_2, "Doc",F,"Doctors",F,F)
b3 = plot_congruence_2(df_2, "Judge",F,"Judges",T,F)
ggarrange(a1, a2, a3, b1, b2, b3, labels = c("", "", "","", "", "","", "", ""),ncol = 3, nrow = 2, widths=c(0.85,0.78,1.15))
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

ggsave("plots/Figure6-CongrencexSupport-politicization.png",width = 10,height = 7,units = "in",dpi = 300)

Figure 7

We now want to look at our results from study 3. First, let’s load the data

df_3 = read.csv('Study 3 anon.csv')

df_3$TrustProf<- df_3$Trust_Prof + df_3$Trust_Prof.0 + df_3$Trust_Prof.1
df_3$PolitProf <- df_3$Polit_Prof + df_3$Polit_Prof.0 + df_3$Polit_Prof.1
df_3$SlantProf <- df_3$Slant_Prof + df_3$Slant_Prof.0 + df_3$Slant_Prof.1

df_3$TrustPolice <- df_3$Trust_police + df_3$Trust_Police.0 + df_3$Trust_Police.1
df_3$PolitPolice <- df_3$Polit_Police + df_3$Polit_Police.0 + df_3$Polit_Police.1
df_3$SlantPolice <- df_3$Slant_Police + df_3$Slant_Police.0 + df_3$Slant_Police.1

Next, we want to look at the relationship betweeen trust and politicization based on folks’ ideology

plot_parties <- function(data, group,include_y,xlab_use,include_legend,trust){
  data$party <- ifelse(is.na(data$Ideology), NA,
                       ifelse(data$Ideology < 4, "Left-leaning",
                              ifelse(data$Ideology == 4, "Central", "Right-leaning")))
  data$party <- factor(data$party, levels = c('Left-leaning','Central','Right-leaning'))
  
  a <- ggplot(data, aes(x=get(paste0('Polit',group)), y = get(paste0(ifelse(trust, "Trust", "Support"),group)), color=party)) + 
    geom_point(alpha = 0.5, size =0.5) + geom_jitter(height = 0.5,width = 0.5,alpha = 0.5, size =0.5) + geom_smooth(method=glm,aes(fill=party)) + 
    scale_color_manual(values=c('blue','gray',"red"))+ scale_fill_manual(values=c('blue','gray',"red"))+
    xlab(paste0('Politicization (',xlab_use,')')) +  
    ylab(ifelse(include_y, ifelse(trust, "Trust", "Support"), ""))  + 
    theme(axis.text=element_text(size=15), 
          axis.title=element_text(size=15),
          axis.title.y = element_text(size=15),
          axis.title.x=element_text(size=15),
          panel.background = element_rect(fill='white'),
          plot.background = element_rect(fill='transparent', color=NA),
          legend.position = ifelse(include_legend,'right',"none"),
          axis.text.x=element_text(size=8, angle=90),
          panel.border = element_rect(colour = "black", fill=NA, size=1))
  return(a)
}

df_reg <- df_3 %>% select(Ideology,ACProfPass,TrustProf,PolitProf) %>% drop_na() %>% filter(ACProfPass == 1)
a1 <- plot_parties(df_reg,'Prof',T,'Professors',F,T)
df_reg <- df_3 %>% select(Ideology,ACPolicePass,TrustPolice,PolitPolice) %>% drop_na() %>% filter(ACPolicePass == 1)
b1 <- plot_parties(df_reg,'Police',F,'Police',T,T)


ggarrange(a1, b1, labels = c("", ""),ncol = 2, nrow = 1, widths=c(0.85,1.15))
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

ggsave("plots/Figure7-Partyxpoliticization-trust.png",width = 8,height = 3.5,units = "in",dpi = 300)

Figure 8

Finally, we’ll make one more figure that is much like the previous, but with support

df_reg <- df_3 %>% select(Ideology,ACProfPass,SupportProf,PolitProf) %>% drop_na() %>% filter(ACProfPass == 1)
a1 <- plot_parties(df_reg,'Prof',T,'Professors',F,F)
df_reg <- df_3 %>% select(Ideology,ACPolicePass,SupportPolice,PolitPolice) %>% drop_na() %>% filter(ACPolicePass == 1)
b1 <- plot_parties(df_reg,'Police',F,'Police',T,F)


ggarrange(a1, b1, labels = c("", ""),ncol = 2, nrow = 1, widths=c(0.85,1.15))
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

ggsave("plots/Figure8-Partyxpoliticization-support.png",width = 8,height = 3.5,units = "in",dpi = 300)